Patient similarity network

A patient similarity network (PSN) is a mathematical model based on graph theory that allows for the visual and analytical exploration of complex relationships among individuals. In this graph representation, nodes represent single patients, while edges measure the degree of clinical, phenotypic, or molecular similarity between them. PSNs are increasingly utilized in clinical research to support clinical decision-making, shifting the focus from general statistical averages to highly personalized treatments, the core concept of precision medicine. By employing machine learning techniques, these networks enable the prediction of patient outcomes and improve patient stratification, facilitating the identification of novel subtypes in complex diseases. The ability to map each patient in relation to others introduces a paradigm where individual complexity is treated as unique, with specific characteristics and therapeutic needs. Furthermore, these networks are intuitive and easy to visualize, offering a natural way to inspect complex, high-dimensional data. This confers explainability for the predictions generated by machine learning models, while effectively maintaining the privacy that each patient deserves.

History and context

Historically, clinical practice and medical research have relied on a "one-size-fits-all" model, which was based on generalized treatments designed for the "average patient". In this paradigm, a standard therapy was prescribed to everyone sharing the same disease label. While this approach has proven successful for many common conditions, it fails to account for the inter-individual variability and underlying molecular differences among patients. Consequently, commonly prescribed drugs may only benefit a small fraction of patients, leaving others as non-responders or even causing adverse side effects. Accounting for these unique features is especially critical in complex diseases which arise from the interplay of multiple biological and environmental factors. To address these limitations, modern healthcare is shifting toward precision medicine. This approach aims to fragment broad diagnostic categories into specific subtypes, tailoring diagnostics and treatments to each patient's unique genetic, molecular and environmental profile. This conceptual shift coincides with the post-genomic era and the advent of high-throughput technologies, which generate massive volumes of heterogeneous patient data at high velocity. Precision medicine relies on integrating these "Big Data", acquired from electronic health records (EHRs), wearable devices, biomarkers, and genetic data. As researchers collect increasingly large datasets combining biological, clinical, and environmental information, advanced computational tools are required to process them. To manage and interpret this scale of data, artificial intelligence and network-based methods have become essential. PSNs were developed within this context as a tool to integrate large amounts of heterogeneous data. By grouping patients based on shared clinical and molecular traits, PSNs enable the identification of highly specific and precise patient subgroups, providing a solid foundation for individualized care.

Network construction procedure

The construction of a patient similarity network involves transforming individual patient profiles into a mathematical graph. In this structure, each patient is represented as a node, and the similarities between their medical or biological profiles are represented as weighted edges. This process generally follows four main steps: data collection, similarity calculation, data integration, and graph sparsification.

Data collection The first step for the construction of the network involves gathering features that could accurately describe the clinical and biological profile of a patients. In order to have a complete understanding of the different subtypes, precision medicine relies on highly heterogeneous datasets, which can be broadly divided in the following categories:

Electronic health records Electronic health records (EHRs) are digital archives including structured clinical data, such as demographics, vital signs, laboratory test results, prescribed medications and formal diagnoses, as well as unstructured data such as clinical notes. Analizing EHRs often represents a challenge, due to the irregular and incomplete nature of data. However, they can be useful to researchers to track clinical trajectories over time about the evolution of the patient state. This makes it possible to map not only a patient's current health but also their entire medical history and disease progression.

Multi-omics data

Advances in high-throughput molecular analysis have revolutionized our understanding of biological processes. The term "omics" refers to the holistic study of the molecules within a biological system, with each discipline focusing on a specific cellular aspect. The complexity of a biological system arises from the interactions among these various omics layers: genomics, epigenomics, transcriptomics, proteomics, and metabolomics. These omics layers can be analyzed individually or integrated. Multi-omics integration yields a more comprehensive and precise patient molecular profile (taking into consideration both static and dynamic cellular aspects), enabling the identification of specific patient subtypes that may share the same phenotypic profile but are driven by different underlying biological processes. This generation of comprehensive data enhances the understanding of disease mechanisms and advances precision medicine approaches. However, integrating these high-dimensional data vectors introduces extreme computational complexity. This difficulty is primarily driven by magnitude imbalances across different omics layers and technical background noise dependent on sequencing platforms. Building a PSN with this data reduces background noise by focusing on the relationships between patients rather than just their raw measurements. Grouping patients this way captures their complete molecular profile, making predictions about survival or treatment responses much more accurate than models relying on a single omics layer.

Medical imaging and wearables

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